出處：http://www.kdnuggets.com/2015/05/r-vs-python-meetup-report.html

資料來源：http://www.r-bloggers.com/milestone-6000-packages-on-cran/

# 抓資料
library(DataTaipei)
rs <- dataSetMetadataSearch(q = "公廁")
data <- resourceAquire(getResources(rs, 1)$resourceId)
# 畫圖
library(ggmap)
qmap("台北火車站", zoom = 16,legend = "topleft") +
  geom_point(aes(x = lng, y = lat), colour = "blue", data = data)

# 抓資料
library(DataTaipei)
rs <- dataSetMetadataSearch(q = "youbike")
data <- resourceAquire(getResources(rs, 1)$resourceId)
# 整理資料
library(dplyr)
data <- dplyr::mutate(data, lat = as.numeric(lat), lng = as.numeric(lng))
# 畫圖
library(ggmap)
qmap("台北火車站", zoom = 15,legend = "topleft") +
  geom_point(aes(x = lng, y = lat, size = bemp), colour = "blue", data = data)

library(DataTaipei)
rs <- dataSetMetadataSearch("氣象")
data <- resourceAquire(getResources(rs, 2)$resourceId[1])
library(dplyr)
data <- dplyr::filter(data, locationName == "臺北市") %>%
  dplyr::mutate(startTime = as.POSIXct(strptime(startTime, "%Y-%m-%dT%H:%M:%S+08:00")), 氣溫=parameterName2) %>%
  dplyr::select(startTime, 氣溫)
library(dygraphs)
library(xts)
xts(data[,-1,with=FALSE], order.by = data$startTime) %>%
  dygraph

# 抓資料
library(DataTaipei)
rs <- dataSetMetadataSearch(q = "不動產")
data <- resourceAquire(getResources(rs, 4)$resourceId)
# 整理資料
library(dplyr)
data <- dplyr::mutate(data, `SDATE`=as.Date(as.character(SDATE + 19110000),"%Y%m%d"))
colnames(data)[-1] <- strsplit(rs$fieldDescription[4], "、", useBytes = TRUE)[[1]]
# 行政區分佈
library(googleVis)
group_by(data, `都市土地使用分區(LANDA_Z)`) %>%
  summarise(count = length(`_id`)) %>%
  gvisBarChart(yvar = "count") %>% 
  plot

m <- dplyr::filter(data, `成交案件類型(CASE_T)` == "買賣") %>%
  lm(formula = `交易單價(萬元/坪)/租賃單價(元/坪)(UPRICE)` ~
     `單價是否含車位(UPNOTE)` +
     `主要建材(MBUILD)` +
     `行政區(DISTRICT)` +
     `都市土地使用分區(LANDA_Z)`)